code: def _update_trainable_params(tape):
      params = tape.get_parameters(trainable_only=False)
      tape.trainable_params = qml.math.get_trainable_indices(params)
  
file_path: pennylane/pennylane/transforms/tape_expand.py
imports: import contextlib
  import pennylane
  from pennylane.operation import has_gen, gen_is_multi_term_hamiltonian, has_grad_method, has_nopar, has_unitary_gen, is_measurement, is_trainable, not_tape
label_desc: Use image node_id 1 for calling a global function with example usage: _update_trainable_params(tape) without return types